Crime hotspot mapping using the crime related factors--a spatial data mining approach

Authors:
Dawei Wang;Wei Ding;Henry Lo;Tomasz Stepinski;Josue Salazar;Melissa Morabito
Affiliations:
Department of Computer Science, University of Massachusetts Boston, Boston, USA 02125-3393;Department of Computer Science, University of Massachusetts Boston, Boston, USA 02125-3393;Department of Computer Science, University of Massachusetts Boston, Boston, USA 02125-3393;Department of Geography, University of Cincinnati, Cincinnati, USA 45221;Department of Computer Science, Rice University, Houston, USA 77005-1827;Department of Criminology and Criminal Justice, University of Massachusetts Lowell, Lowell, USA 01854
Venue:
Applied Intelligence
Year:
2013

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Abstract

The technique of Hotspot Mapping is widely used in analysing the spatial characteristics of crimes. The spatial distribution of crime is considered to be related with a variety of socio-economic and crime opportunity factors. But existing methods usually focus on the target crime density as input without utilizing these related factors. In this study, we introduce a new crime hotspot mapping tool--Hotspot Optimization Tool (HOT). HOT is an application of spatial data miming to the field of hotspot mapping. The key component of HOT is the Geospatial Discriminative Patterns (GDPatterns) concept, which can capture the differences between two classes in a spatial dataset. Experiments are done using a real world dataset from a northeastern city in the United States and the pros and cons of utilizing related factors in hotspot mapping are discussed. Comparison studies with the Hot Spot Analysis tool implemented by Esri ArcMap 10.1 validate that HOT is capable of accurately mapping crime hotspots.